Aldo G. Orozco-Lugo

Frame/Training Sequence Synchronization and DC-Offset Removal for (Data-Dependent) Superimposed Training Based Channel Estimation

Over the last few years there has been growing interest in performing channel estimation via superimposed training (ST), where a training sequence is added to the information-bearing data, as opposed to being time-division multiplexed with it. Recent enhancements of ST are data-dependent ST (DDST), where an additional data-dependent training sequence is also added to the information-bearing signal, and semiblind approaches based on ST. In this paper, along with the channel estimation, we consider new algorithms for training sequence synchronization for both ST and DDST and block (or frame) synchronization (BS) for DDST (BS is not needed for ST). The synchronization algorithms are based on the structural properties of the vector containing the cyclic means of the channel output. In addition, we also consider removal of the unknown dc offset that can occur due to using first-order statistics with a non-ideal radio-frequency receiver. The subsequent bit error rate (BER) simulations (after equalization) show a performance not far removed from the ideal case of exact synchronization. While this is the first synchronization algorithm for DDST, our new approach for ST gives identical results to an existing ST synchronization method but with a reduced computational burden. In addition, we also present analysis of BER simulations for time-varying channels, different modulation schemes, and traditional time-division multiplexed training. Finally, the advantage of DDST over (conventional, non semi-blind) ST will reduce as the constellation size increases, and we also show that even without a BS algorithm, DDST is still superior to conventional ST. However, iterative semiblind schemes based upon ST outperform DDST but at the expense of greater complexity

Enhanced Channel Estimation Using Superimposed Training Based on Universal Basis Expansion

In this correspondence, an approach to enhance the quality of superimposed training (ST) based channel estimation procedures is proposed. The approach is based on postprocessing the estimated channel. This postprocessing is performed with the projection of the estimated channel onto a set of orthogonal functions known as the Universal Basis (UB), that were defined in [A. G. Orozco-Lugo, R. Parra-Michel, D. McLernon, and V. Kontorovitch, ldquoEnhancing the Performance of the CR Blind Channel Estimation Algorithm Using the Karhunen-Loeve Expansion,rdquo Proceedings of the ICASSP, May 2002, pp. III-2653-III-2656 ]. The projection leads to improved channel estimation when compared to raw ST methods. We demonstrate the enhanced performance of the proposed technique by means of both theoretical formulas and simulation results, focusing on data dependent ST.

Blind channel equalization using chirp modulating signals

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. In this paper, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a batch-type algorithm is obtained for calculating the equalizers coefficients. Conditions on the chirp sequence parameters are obtained that guarantee an equalization solution. A low-complexity adaptive algorithm is also proposed. Finally, extensive simulations, and comparisons with other well-known blind techniques, illustrate the excellent performance of this algorithm.

Time-Varying Channel Estimation Using Two-Dimensional Channel Orthogonalization and Superimposed Training

In this correspondence, a method is presented for estimating double-selective channels using superimposed training (ST). The estimator is based on a subspace projection of the time-varying channel onto a set of two dimensional orthogonal functions. These functions are formed via the outer product of the discrete prolate spheroidal basis vectors and the universal basis vectors. This approach allows the channel to be expanded in both the time-delay and time dimensions with the fewest parameters when incomplete channel statistics are given. This correspondence also provides a theoretical performance analysis of the estimation algorithm and its corroboration via simulations. It is shown that this new method provides an enhancement in channel estimation when compared with state-of-the-art approaches.

Widely Linear System Estimation Using Superimposed Training

In this correspondence, the use of superimposed training (ST) as a mean to estimate the finite impulse response (FIR) components of a widely linear (WL) system is proposed. The estimator here presented is based on the first-order statistics of the signal observed at the output of the system and its variance is independent of the channel components if suitable designed training sequences are employed. The construction of such sequences having constant magnitude both in time and frequency domains is also addressed.

Simulation of Wide Band Channels with non-separable scattering functions

In this paper a new and constructive method useful to simulate Wide Band Channels with non-separable scattering function via the filtering approach is provided. This method is based on the approximation of the Channel by the eigenfunctions taken from the Karhunen-Loève expansion of the Channels. ensemble autocorrelation function at Δt=0. Channel realizations are performed via the filtering of an independent gaussian random process vector by some filter matrix, which is obtained from some manipulations of the correlation statistics of eigenfunctions. An example to clarify this proposal is also presented.

On the MAC/Network/Energy Performance Evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR Routing Protocols

Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.

Iterative Mean Removal Superimposed Training for SISO and MIMO Channel Estimation

This contribution describes a novel iterative radio channel estimation algorithm based on superimposed training (ST) estimation technique. The proposed algorithm draws an analogy with the data dependent ST (DDST) algorithm, that is, extracts the cycling mean of the data, but in this case at the receivers end. We first demonstrate that this mean removal ST (MRST) applied to estimate a single-input single-output (SISO) wideband channel results in similar bit error rate (BER) performance in comparison with other iterative techniques, but with less complexity. Subsequently, we jointly use the MRST and Alamouti coding to obtain an estimate of the multiple-input multiple-output (MIMO) narrowband radio channel. The impact of imperfect channel on the BER performance is evidenced by a comparison between the MRST method and the best iterative techniques found in the literature. The proposed algorithm shows a good tradeoff performance between complexity, channel estimation error, and noise immunity.

An efficient Multi-Parent Hierarchical routing protocol for WSNs

Wireless sensor networks (WSNs) nodes are commonly designed to work with limited resources of memory, energy and processing. The routing protocol is one of the key components of WSNs and its features impact network performance significantly. We present an efficient Multi-Parent Hierarchical (MPH) routing protocol for wireless sensor networks; its main goal is to achieve reliable delivery of data in a single sink scenario while keeping low overhead, reduced latency and low energy consumption. The main features of MPH are self-configuration, hierarchical topology, persistence according to link quality, and source routing from sink to nodes. Network performance simulations of the MPH routing protocol are carried out and compared with two popular protocols, AODV, DSR and with the well-known algorithm ZTR (Zigbee Tree Routing). Results show that for the single sink scenario, the MPH protocol has an energy saving of 35% against AODV and DSR protocols and 8% compared with ZTR. MPH has 27% less overhead compared with AODV and DSR. And MPH presents a 10% increase in packet delivery compared with AODV, DSR and ZTR. Finally, we present a real WSN built based on the MPH protocol, which works satisfactorily, providing an experimental demonstration of the capabilities of the protocol.

A multi-cycle fixed point square root module for FPGAs

This paper presents a module that solves the square root by obtaining a number of more significant bits from a look-up table as an approximate root. A set of possible roots are then appended and squared for comparison to the original radicand, finely tuning the calculation. The module stops as soon as it finds an exact root, therefore not all entries take the same number of cycles, reducing the number of iterations required for full resolution. The proposed FPGA module overcomes a Xilinx’s logiCORE IP in terms of resources utilization and in several cases latency due to its flexible structure configuration.